In a video delivery service, providing relevant recommendations to a user may increase the use frequency of the service. The video delivery service may use a recommendation algorithm that is based on static patterns learned from user history data. However, user preferences and behaviors often change over time. To capture these changes, the video delivery service may periodically update the static patterns for the recommendation algorithm. Oftentimes, these updates are not frequent enough to keep up with the changing user preferences. Additionally, when offering an interactive live television service in addition to video on-demand, user preferences may change even more often. Further, the complexity of generating recommendations may increase due to the difference in viewing habits for the live television service and the video on-demand service. Thus, the use of static patterns may not be able to generate relevant recommendations for users using both services.